Abstract

This paper proposes to enhance similarity-based classification by virtual attributes from imperfect domain theories. We analyze how properties of the domain theory, such as partialness and vagueness, influence classification accuracy. Experiments in a simple domain suggest that partial knowledge is more useful than vague knowledge. However, for data sets from the UCI Machine Learning repository, we show that vague domain knowledge which in isolation performs at chance level can substantially increase classification accuracy when being incorporated into similarity-based classification.

Citations